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npu_memory_allocator.cc 4.8 kB

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  1. /**
  2. * Copyright 2019-2020 Huawei Technologies Co., Ltd
  3. *
  4. * Licensed under the Apache License, Version 2.0 (the "License");
  5. * you may not use this file except in compliance with the License.
  6. * You may obtain a copy of the License at
  7. *
  8. * http://www.apache.org/licenses/LICENSE-2.0
  9. *
  10. * Unless required by applicable law or agreed to in writing, software
  11. * distributed under the License is distributed on an "AS IS" BASIS,
  12. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13. * See the License for the specific language governing permissions and
  14. * limitations under the License.
  15. */
  16. #include "npu_memory_allocator.h"
  17. #include <mutex>
  18. #include "framework/common/debug/log.h"
  19. #include "graph/manager/graph_caching_allocator.h"
  20. #include "graph/manager/graph_mem_allocator.h"
  21. #include "graph/manager/rdma_pool_allocator.h"
  22. namespace ge {
  23. namespace hybrid {
  24. const size_t kPaddingUnit = 2;
  25. size_t kMaxHbmMemorySize = 1024UL * 1024UL * 1024UL * 1024UL; // 1024G
  26. std::map<uint32_t, std::unique_ptr<NpuMemoryAllocator>> NpuMemoryAllocator::allocators_;
  27. std::mutex NpuMemoryAllocator::mu_;
  28. AllocationAttr::AllocationAttr(int padding, void *try_reuse_addr, MemStorageType mem_type)
  29. : padding_(padding), try_reuse_addr_(try_reuse_addr), mem_type_(mem_type) {}
  30. AllocationAttr::AllocationAttr(int padding) : AllocationAttr(padding, nullptr) {}
  31. AllocationAttr::AllocationAttr(void *try_reuse_addr) : AllocationAttr(0, try_reuse_addr) {}
  32. NpuMemoryAllocator *NpuMemoryAllocator::GetAllocator() {
  33. int32_t device_id = 0;
  34. if (rtGetDevice(&device_id) != RT_ERROR_NONE) {
  35. GELOGE(RT_FAILED, "Failed to get device id");
  36. return nullptr;
  37. }
  38. GELOGD("Got device id = %d from context", device_id);
  39. return GetAllocator(static_cast<uint32_t>(device_id));
  40. }
  41. NpuMemoryAllocator::NpuMemoryAllocator(uint32_t device_id) : device_id_(device_id) {}
  42. void *NpuMemoryAllocator::Allocate(std::size_t size, AllocationAttr *attr) {
  43. size_t allocate_size = size;
  44. MemStorageType mem_type = HBM;
  45. if (attr != nullptr) {
  46. mem_type = attr->mem_type_;
  47. }
  48. if (allocate_size == 0) {
  49. GELOGE(MEMALLOC_FAILED, "Memory size is 0, device_id = %u, size = %zu", device_id_, allocate_size);
  50. return nullptr;
  51. }
  52. void *buffer = nullptr;
  53. if (mem_type == RDMA_HBM) {
  54. buffer = MemManager::Instance().RdmaPoolInstance(RT_MEMORY_HBM).Malloc(allocate_size, device_id_);
  55. } else if (mem_type == HOST_DDR) {
  56. buffer = malloc(allocate_size);
  57. } else {
  58. if (allocate_size > kMaxHbmMemorySize) {
  59. GELOGE(PARAM_INVALID, "Invalid HBM memory size: %zu", allocate_size);
  60. return nullptr;
  61. }
  62. void *try_reuse_addr = nullptr;
  63. int padding = kDefaultPadding;
  64. if (attr != nullptr) {
  65. try_reuse_addr = attr->try_reuse_addr_;
  66. if (attr->padding_ > 0) {
  67. padding = attr->padding_;
  68. }
  69. }
  70. // padding up to multiple of padding, and add extra padding
  71. allocate_size = (size + kPaddingUnit * padding - 1) / padding * padding;
  72. GELOGD("Padding size %ld by %d. final size = %zu.", size, padding, allocate_size);
  73. buffer = MemManager::Instance()
  74. .CachingInstance(RT_MEMORY_HBM)
  75. .Malloc(allocate_size, reinterpret_cast<uint8_t *>(try_reuse_addr), device_id_);
  76. }
  77. if (buffer == nullptr) {
  78. GELOGE(MEMALLOC_FAILED, "Failed to malloc memory, device_id = %u, size = %zu", device_id_, allocate_size);
  79. return nullptr;
  80. }
  81. GELOGI("Allocating buffer of size %zu successfully. device_id = %u, address = %p", allocate_size, device_id_, buffer);
  82. return buffer;
  83. }
  84. void NpuMemoryAllocator::Deallocate(void *data, MemStorageType mem_type) {
  85. GELOGI("To deallocating buffer, addr = %p", data);
  86. if (data != nullptr) {
  87. GELOGI("Deallocating buffer successfully. addr = %p", data);
  88. if (mem_type == RDMA_HBM) {
  89. MemManager::Instance().RdmaPoolInstance(RT_MEMORY_HBM).Free(reinterpret_cast<uint8_t *>(data), device_id_);
  90. } else if (mem_type == HOST_DDR) {
  91. free(data);
  92. } else {
  93. MemManager::Instance().CachingInstance(RT_MEMORY_HBM).Free(reinterpret_cast<uint8_t *>(data), device_id_);
  94. }
  95. }
  96. }
  97. NpuMemoryAllocator *NpuMemoryAllocator::GetAllocator(uint32_t device_id) {
  98. std::lock_guard<std::mutex> lk(mu_);
  99. auto it = allocators_.find(device_id);
  100. if (it == allocators_.end()) {
  101. auto allocator = std::unique_ptr<NpuMemoryAllocator>(new (std::nothrow) NpuMemoryAllocator(device_id));
  102. if (allocator == nullptr) {
  103. return nullptr;
  104. }
  105. allocators_.emplace(device_id, std::move(allocator));
  106. }
  107. return allocators_[device_id].get();
  108. }
  109. void NpuMemoryAllocator::DestroyAllocator() {
  110. std::lock_guard<std::mutex> lk(mu_);
  111. int device_id = 0;
  112. allocators_.erase(device_id);
  113. }
  114. } // namespace hybrid
  115. } // namespace ge

图引擎模块(GE)是MindSpore的一个子模块,其代码由C++实现,位于前端模块ME和底层硬件之间,起到承接作用。图引擎模块以ME下发的图作为输入,然后进行一系列的深度图优化操作,最后输出一张可以在底层硬件上高效运行的图。GE针对昇腾AI处理器的硬件结构特点,做了特定的优化工作,以此来充分发挥出昇腾AI处理器的强大算力。在进行模型训练/推理时,GE会被自动调用而用户并不感知。GE主要由GE API和GE Core两部分组成,详细的架构图如下所示